Three-dimensional ultrasound imaging method and device

ABSTRACT

The three-dimensional ultrasound imaging method comprise emitting an ultrasonic wave to a fetal head; receiving an ultrasonic echo to acquire an ultrasonic echo signal; acquiring three-dimensional volume data of the fetal head according to the ultrasonic echo signal; detecting a median sagittal section from the three-dimensional volume data according to features of the median sagittal section of the fetal head; determining a facing orientation of the fetal head in the median sagittal section; displaying the median sagittal section as an image suitable for observation according to the facing orientation of the fetal head.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.15/800,387, filed Nov. 1, 2017, for THREE-DIMENSIONAL ULTRASOUND IMAGINGMETHOD AND DEVICE, which is a continuation of PCT Application No.PCT/CN2015/078494, filed May 7, 2015, both of which are incorporatedherein by reference.

TECHNICAL FIELD

The present disclosure relates to medical ultrasound imaging, and moreparticularly to a three-dimensional ultrasound imaging method anddevice.

BACKGROUND

Ultrasound imaging is generally used by doctors to inspect tissueswithin a human body. A doctor may place the probe on the surface of theskin above the target tissues, after which ultrasound images of thetissue may be obtained. Due to its safety, convenience, non-invasivenessand low cost, ultrasound imaging has become one of the primary tools toaid in medical diagnoses. Obstetrics is one of the fields in whichultrasound imaging is most widely used. With the use of ultrasound, theeffects of X-rays and the like on the mother and the fetus may beavoided making it superior to other imaging modalities. With ultrasoundimaging, not only may observation and measurement of the fetus inmorphology be conducted, but also information about physiology andpathology, such as respiratory and urinary information, may be obtained,thereby evaluating the health and growth status of the fetus.

When inspecting the nervous system of the fetus, the corpus callosum andthe cerebellar vermis are two very important inspection targets. Thecorpus callosum is the largest conjugate fiber between the hemispheresof the brain, and is responsible for the communication between thecerebral hemispheres. Deficiency or hypogenesis of the corpus callosumwill lead to several complications, such as epilepsy, mental retardationor dyskinesia. Deficiency or hypogenesis of the cerebellar vermis is asymptom of Dandy-Walker syndrome. Fifty percent of patients with theDandy-Walker syndrome show signs mental retardation and usually havechromosome abnormalities and other deformities with poor prognoses andhigh mortality rates. Accordingly, the abnormalities of the corpuscallosum and the cerebellar vermis represent critical diseases. If theyare not found during the prenatal examination, they could bring hugemental and economic burdens to the family of the patient and thesociety. However, the corpus callosum and the cerebellar vermis are veryeasy to be misdiagnosed or missed during the inspection of the nervoussystem of the fetus. The reason is that it is very difficult to obtainthe median sagittal section image of the fetus, which is the best imagefor observing the corpus callosum and the cerebellar vermis, by aconventional two-dimensional ultrasound imaging due to the affects ofthe factors such as fetal position, amniotic fluid, obstruction of thenasal bone, and skill of the doctors, etc. Even if the image of themedian sagittal section can be obtained, doing so may take a long time.Accordingly, many doctors have to indirectly inspect the corpus callosumand the cerebellar vermis by images of other sections (such as thecerebellum section or the thalamus section, etc.), increasing the riskof misdiagnosis.

Recently, with the widespread use of the three-dimensional ultrasoundimaging, some doctors perform a three-dimensional scanning on the fetusstarting from the biparietal diameter section, obtain an image of themedian sagittal section of the fetus by geometric transforms of 3Dultrasound image data, such as manual rotation and translation, and theninspect the corpus callosum and the cerebellar vermis through thismedian sagittal section image. Although the median sagittal sectionimage obtained by this method may have relatively lower quality than aconventional two-dimensional image, the corpus callosum and thecerebellar vermis can be relatively clearly displayed, and abnormalitiesof the corpus callosum and the cerebellar vermis can be determinedquickly and precisely. In order to image the median sagittal section inthree-dimensional space by manual rotation and translation, the doctorsmust understand the three-dimensional space very well. However, mostdoctors have no science and engineering background and lack therequisite understanding of three-dimensional space. Therefore it is verydifficult for doctors to obtain the median sagittal section image fromvolume data.

SUMMARY

In one embodiment, a three-dimensional ultrasound imaging method anddevice are provided, which can three-dimensionally image the fetal head,automatically extract the median sagittal section image of the fetalhead, determine the orientation of the fetal head, and adjust the mediansagittal section image based on the determined orientation such that themedian sagittal section image is suitable for human observation.

The technical solutions provided by the embodiments of the presentdisclosure may include the following. In one embodiment, athree-dimensional ultrasound imaging method is provided. The method mayinclude transmitting ultrasound waves to a fetal head; receivingultrasound echoes to obtain ultrasound echo signals; obtainingthree-dimensional volume data of the fetal head using the ultrasoundecho signals; extracting a median sagittal section image from thethree-dimensional volume data based on characteristics of a mediansagittal section of a fetal head; detecting image regions representingspecific tissue areas of the fetal head in the median sagittal sectionimage and/or in a section image which is parallel to or intersects withthe median sagittal section image; determining an orientation of thefetal head in the median sagittal section image based on the imageregions; and rotating the median sagittal section image based on theorientation of the fetal head such that in the rotated median sagittalsection image the feta head is in a pre-set orientation, or marking theorientation of the fetal head in the median sagittal section image.

In one embodiment, a three-dimensional ultrasound imaging device isprovided. The device may include a probe which transmits ultrasoundwaves to a fetal head and receives ultrasound echoes to obtainultrasound echo signals; a three-dimensional imaging unit which obtainsthree-dimensional volume data of the fetal head using the ultrasoundecho signals, extracts a median sagittal section image from thethree-dimensional volume data based on characteristics of a mediansagittal section of a fetal head, determines an orientation of the fetalhead in the median sagittal section image, and rotates the mediansagittal section image based on the orientation of the fetal head suchthat in the rotated median sagittal section image the fetal head is in apre-set orientation or marks the orientation of the fetal head in themedian sagittal section image; and a display which display the mediansagittal section image.

In the embodiments of the present disclosure, ultrasound scanning may beperformed on the fetus to obtain the three-dimensional volume data ofthe fetal head, and the median sagittal section image of the fetal headmay be automatically extracted according to the obtainedthree-dimensional volume data. Thereafter, the orientation of the fetalhead in the median sagittal section image may be automaticallydetermined (e.g., determining whether the fetus is upside down and whichside the face is towards), and the median sagittal section image may beadjusted based on the results of the determination such that thedisplayed median sagittal section image will be suitable for humanobservation. Accordingly, the doctor can more easily identify andobserve the median sagittal section image of the fetal head.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to describe the technical solutions of the embodiments moreclearly, the drawing to be used in the description of the embodimentwill be briefly described, where the same reference number representsthe same part.

FIG. 1 is a schematic block diagram of a three-dimensional ultrasoundimaging device in one embodiment;

FIG. 2 is a flow chart of a three-dimensional ultrasound imaging methodin one embodiment;

FIG. 3 schematically shows a three-dimensional volume data in oneembodiment;

FIG. 4 schematically shows the position of the median sagittal sectionin a fetal head;

FIG. 5 schematically shows a median sagittal section image of a fetalhead;

FIG. 6 schematically shows the image of the section L1 in FIG. 5 ;

FIG. 7 schematically shows the image of the section L2 in FIG. 5 ;

FIG. 8 schematically shows the median sagittal section images in whichthe fetal head is upside down and upright, respectively.

FIG. 9 is a flow chart for determining the orientation of the fetal headin the median sagittal section image in one embodiment;

FIG. 10 schematically shows the determination of the orientation of theskull;

FIG. 11 is a flow chart for determining the orientation of the fetalhead in the median sagittal section image in one embodiment;

FIG. 12 is a flow chart of a three-dimensional ultrasound imaging methodin one embodiment;

FIG. 13 is a flow chart for determining the orientation of the face inone embodiment;

FIG. 14 is a flow chart of a three-dimensional ultrasound imaging methodin one embodiment;

FIG. 15 is a flow chart for extracting the median sagittal section imagein one embodiment;

FIG. 16 schematically shows the plane and the plane parameters thereofin a three-dimensional space;

FIG. 17 schematically shows a three-dimensional Hough matrix in oneembodiment;

FIG. 18 schematically shows a process of a weighted Hough transform inone embodiment;

FIG. 19 schematically shows a process of a random Hough transform in oneembodiment;

FIG. 20 schematically shows a process for detecting the plane determinedby the selected characteristic points in one embodiment;

FIG. 21 schematically shows the flow chart for extracting the mediansagittal section image in one embodiment;

FIG. 22 is a flow chart for extracting the brain midline in oneembodiment;

FIG. 23 is a flow chart for extracting the median sagittal section imagein one embodiment;

FIG. 24 is a flow chart for extracting the median sagittal section imagein one embodiment; and

FIGS. 25 and 26 schematically show the icons used for marking theorientation of the fetal head in one embodiment.

DETAILED DESCRIPTION

In one embodiment, a three-dimensional ultrasound imaging device may beprovided. A block diagram of the device is shown in FIG. 1 . Thethree-dimensional ultrasound imaging device may include a probe 102, atransmitting/receiving switch 103, a transmitting circuit 104, areceiving circuit 105, a beam-forming unit 106, a signal processing unit107, a three-dimensional imaging unit 108 and a display 109. Thetransmitting circuit 104 may transmit a group of pulses which have beenfocused by delay to the probe 102 to excite the probe 102 transmitultrasound beams to the tissue to be examined (no shown in the figure).After a certain time, the probe 102 may receive the ultrasound echoeswhich are reflected by the tissue and carry the information related tothe tissue, and convert the ultrasound echoes into electric signals. Thereceiving circuit 105 may receive the ultrasound echo signals convertedinto electric signals and send the ultrasound echo signals to thebeam-forming unit 106. In the beam-forming unit 106, focus delay,weighting and channel summation may be performed on the ultrasound echosignals. Thereafter, signal processing may be performed on theultrasound echo signals in the signal processing unit 107. The signalsprocessed by the signal processing unit 107 may be sent to thethree-dimensional imaging unit 108. By the processing in thethree-dimensional processing unit 108, visual information such asthree-dimensional images etc. may be obtained, which may be sent to thedisplay 109.

After the probe 102 performs the scanning for one scan cycle, thesignals processed by the signal processing unit 107 may form one volumeof three-dimensional volume data in polar coordinate. Thethree-dimensional volume data in polar coordinate may be reconstructedto convert the polar coordinate volume data into Cartesian coordinatevolume data, thereby obtaining a volume of three-dimensional volume datain Cartesian coordinate. Then, the three-dimensional unit 108 mayprocess the three-dimensional volume data in Cartesian coordinate toobtain the visual information. The visual information may be displayedon the display 109.

The three-dimensional imaging unit 108 of the three-dimensionalultrasound imaging device in the present embodiment may further includea sub-unit which may be used to automatically extract the mediansagittal section image of the fetus. This sub-unit may automaticallyextract the median sagittal section image from the obtainedthree-dimensional volume data of the head of the fetus, and process themedian sagittal section image to detect the orientation of the fetalhead in the median sagittal section image (for example, the fetal headand/or the fetal face is towards the top, towards the top left, towardsthe top right, towards the left, towards the right, towards the bottom,towards the bottom left, towards the bottom right or towards otherdirection, etc.). Thereafter, the sub-unit may rotate the mediansagittal section image such that, in the rotated median sagittal sectionimage, the fetal head is in a pre-set orientation (for example, the topof the fetal head is towards the top or the bottom or any other desireddirection, or the fetal face is towards the top or the bottom or anyother desired direction, etc., so as, for example, to facilitate theobservation of the doctor or be suitable for the habits of the doctor,etc.), and/or mark the detected orientation of the fetal head in themedian sagittal section image and display the processed median sagittalsection image (described in detail below).

The flow chart of a three-dimensional ultrasound imaging methodimplemented by the three-dimensional ultrasound imaging device providedin the present embodiment is shown in FIG. 2 . In step 21, thethree-dimensional ultrasound imaging device may performthree-dimensional scanning on the fetal head. The three-dimensionalultrasound imaging device may transmit the ultrasound beams to the fetalhead and receive the ultrasound echoes to obtain the ultrasound echosignals. The ultrasound echo signals may be processed as described aboveto obtain the three-dimensional volume data of the fetal head (referredto as “three-dimensional volume data” hereinafter). The specific stepsfor three-dimensionally scanning the scanning target and processing theultrasound echo signals to obtain the three-dimensional volume data maybe similar to the three-dimensional scanning and imaging methods andwill not be described in detail here. By step 21, at least one volume ofthree-dimensional volume data of the fetal head may be obtained. Forexample, a volume of three-dimensional volume data may be shown in FIG.3 . The volume of three-dimensional volume data may include F frames ofimages, each of which may have a size of W×H, where W is the width ofthe image and H is the height of the image. In addition, in FIG. 3 , thewidth direction of the image may be defined as the X direction, theheight direction of the image may be defined as the Y direction, and thearrangement direction of the multiple frames of image may be defined asthe Z direction. However, the X, Y, and Z directions may also be definedin other ways.

After the three-dimensional volume data is obtained in step 21, in oneembodiment, it is desired to automatically extract the median sagittalsection image of the fetal head from the three-dimensional volume data.

FIG. 4 schematically shows the position of the median sagittal sectionin a fetal head, where the line D represents the median sagittal sectionof the fetal head. FIG. 5 shows a schematic view of a median sagittalsection image of a fetal head, which shows that the image of the mediansagittal section will include important information about the corpuscallosum, the cerebellar vermis and the cavum septi pellucid (CSP) ofthe fetus. Furthermore, the cisterna cerebellomedullaris, theinterthalamic adhesion, and the fourth ventricle, etc. of the fetus mayalso be observed in the median sagittal section image of a fetal head.Therefore, automatically extracting and displaying the median sagittalsection image of a fetal head can provide important information to thedoctors and thus greatly facilitate the observation of the doctors tothe fetus. FIG. 6 and FIG. 7 schematically show the schematic views ofthe section images L1 and L2 which are perpendicular to the mediansagittal section image of a fetal head, respectively.

The inventors have found that, among the images of a fetal head, themedian sagittal section image has some special characteristics. Forexample, among all section images of the three-dimensional image of afetal head, the median sagittal section image as a whole has larger grayvalue than surrounding areas. That is, in the three-dimensional image ofa fetal head, the median sagittal section image appears as a sectionimage which obviously has larger gray value than the surrounding areas,in other words, which has larger brightness than the surrounding areas.In addition, in a fetal head, the tissue structures outside both sidesof the median sagittal section are approximately symmetrical withrespect to the median sagittal section. Therefore, the image data of thethree-dimensional image of a fetal head outside both sides of the mediansagittal section image will have approximate symmetry with respect tothe median sagittal section image. Furthermore, in a fetal head, themedian sagittal section is located at the center position of the fetalhead. Therefore, in the three-dimensional image of a fetal head, anothersection image which intersects with the median sagittal section imagewill contain information of the intersection line of said anothersection image with the median sagittal section image. In said anothersection image, the intersection line of said another section image withthe median sagittal section image appears as a line with higherbrightness, i.e. the brain midline. The collection of the brain midlineswill form the median sagittal section image. In some embodiments of thepresent disclosure, these characteristics of the median sagittal sectionimage of a fetal head may be used to detect or identify the mediansagittal section image from the three-dimensional volume data of a fetalhead.

Accordingly, in one embodiment, in step 23, a median sagittal sectionimage may be extracted from the three-dimensional volume data obtainedin step 21 using the characteristic(s) of the median sagittal sectionimage of a fetal head (for example, the gray characteristic).

In the present embodiment, the entire or a part of the three-dimensionalvolume data of the fetal head of obtained in step 21 may be used toacquire the median sagittal section image. For example, the part of thethree-dimensional volume data where the median sagittal section is mostlikely located may be used to extract the median sagittal section image,while the part where it is obviously impossible for the median sagittalsection to be located may be excluded from the extraction process. Forexample, since the median sagittal section of a fetal head is alongitudinal section (i.e., a section in the direction from the top tothe neck) located at the center position of a fetal head, it will beimpossible for the median sagittal section image to be located in theregions at the edge of the fetal head. Such regions thus can be excludedfrom the extraction process.

In the embodiments, a variety of methods may be used to extract themedian sagittal section image from the three-dimensional volume data.For example, as mentioned above, the median sagittal section image willhave larger gray value than surrounding areas in the three-dimensionalvolume data. Therefore, in one embodiment, this characteristic may beused to extract the median sagittal section image from thethree-dimensional volume data using digital image processing methods,such as image segmentation methods.

In one embodiment, the automatic extraction of the sagittal sectionimage may essentially be indicating the location of the sagittal sectionimage in the three-dimensional volume data. However, the expression ofthe result of the extraction may vary, such as the plane equation, thetranslation (in X, Y and Z directions) and rotation (about the X axis, Yaxis and Z axis) of the sagittal section image with respect to theorigin of the coordinate system, the transformation matrix of thesagittal section image with respect to the original coordinate system(in general one 4×4 matrix can represent the transformation relationbetween two coordinate systems), and even the spatial coordinates ofthree points (since three points can determine a plane), etc. Theseexpressions may essentially be indication of the location of a plane inthe coordinate system of a three-dimensional volume data, and may beconverted to each other. In the embodiments, the expression of planeequation is used for the convenience of description. However, thepresent disclosure is not limited to the expression of the planeequation. Rather, other expressions described above or in the art mayalso be used. The expressions of the result of the extraction of thesagittal section image only differ in form, which does not affect thesubstance of the present disclosure, and therefore are all consideredwithin the scope of the present disclosure.

After the median sagittal section image of the fetal head is extracted,it may be displayed on the display so as to facilitate the observationof the doctor to the fetal head. However, due to the fetal position andthe direction of the probe, the extracted median sagittal section imagemay be upside down (i.e. the fetal head is towards the bottom of theimage, as shown in left view of FIG. 8 ) or in other orientation notsuitable for the observation of the doctor. Since the median sagittalsection image is very difficult to be obtained using conventionaltwo-dimensional ultrasound imaging, most doctors are not familiar withthe median sagittal section image. Furthermore, the resolution of themedian sagittal section image is generally poor. Therefore, there aredifficulties for the doctors to identify the median sagittal sectionimage. If the extracted median sagittal section image is upside down orin other orientation not suitable for the observation by the doctor.Therefore, in the present embodiment, after the median sagittal sectionimage of the fetal head is extracted, in step 25, the orientation of thefetal head in the median sagittal section image (for example, the fetalhead and/or the fetal face is towards the top, towards the top left,towards the top right, towards the left, towards the right, towards thebottom, towards the bottom left, towards the bottom right or towardsother direction, etc.) may further be detected. When it is detected thatthe orientation of the fetal head in the median sagittal section imageis not convenient for the doctor to observe, the median sagittal sectionimage may be rotated such that in the rotated median sagittal sectionimage the fetal head is in a predetermined or desired orientation (forexample, the top of the fetal head is towards the top or the bottom orany other desired direction, or the fetal face is towards the top or thebottom or any other desired direction, etc., so as to, for example,facilitate the observation of the doctor or be suitable for the habitsof the doctor, etc.), and/or the detected orientation of the fetal headmay be marked in the median sagittal section image. When displaying themedian sagittal section image (i.e. step 29), the rotated or markedmedian sagittal section image may be displayed so as to facilitate theobservation of the doctor, as shown in FIG. 8 .

In one embodiment, the orientation of the fetal head in the mediansagittal section image may be obtained by the image regions representingspecific tissue areas of the fetal head in the median sagittal sectionimage and/or in the section image in the three-dimensional volume datawhich is parallel to or intersects with the median sagittal sectionimage.

In one embodiment, the specific tissue areas may be at least twospecific tissue areas which have certain mutual positional relationshipin the fetal head (such as eye and nose, eye and mouth, mouth and nose,eye, nose and/or mouth and cavum septi pellucid, or eye, nose and/ormouth and other tissue of the fetal head, etc.). In the presentembodiment, at least two image regions representing at least two ofthese tissue areas may be extracted or detected from the median sagittalsection image and/or from the section image which is parallel to orintersects with the median sagittal section image, and the orientationof the fetal head may be determined based on the mutual positionalrelationship of these image regions (for example, in the fetal head, thetop of the head is always in the direction from mouth to eye, from mouthto nose or from nose to eye, etc.). When the mutual positionalrelationship of these image regions is determined, the orientation ofthe fetal head may be determined according to the mutual positionalrelationship.

In one embodiment, the specific tissue areas may be the tissue areas inthe fetal head which have directional characteristics. In the presentdisclosure, the tissue area having directional characteristics may bethe tissue area which itself or whose position contains the informationbeing able to indicate the orientation of the fetal head, such as theskull or the skullcap (whose bending direction indicates the orientationof the fetal head), the cavum septi pellucid (whose orientation andposition can indicate the orientation of the fetal head), or mouth, eyeand nose (which are always located at the face side of the fetal head,thereby the orientation of the fetal head may be indicated by theposition thereof), etc.

In these embodiments, one or more image regions representing the tissueareas may be extracted or detected from the median sagittal sectionimage and/or from the section image which is parallel to or intersectswith the median sagittal section image, and the orientation of the fetalhead may be determined according to the positions and/or shapes of theseimage regions (for example, the positions or side at which the eye andthe mouth are located are always the front portion or front side of thefetal head, and the orientation of the head may be determined accordingto the bending direction of the skull, etc.). Hereinafter, examples willbe described in which the skull and the cavum septi pellucid serve asthe specific tissue areas.

In one embodiment, the orientation of the fetal head in the mediansagittal section image may be detected or identified utilizing thestructures in the three-dimensional volume data which have directionalcharacteristics. The applicant found that the skull is represented ashigh brightness in the three-dimensional image of the fetal head.Therefore, the orientation of the skull may be used to determine theorientation of the fetal head. In the present embodiment, theorientation of the skull in the median sagittal section image or in theimage parallel to the median sagittal section image may be detected todetermine the orientation of the fetal head. The process of thedetection is shown in FIG. 9 .

Step 252: extracting skull characteristics from selected section imageto obtain candidate regions representing the skull. Since in theultrasound image the skull is represented as high brightness while thebrightness of the area at both sides of the skull reduced gradually,multiple methods for extracting the skull characteristics may bedesigned based on such feature. For example, in one embodiment, based onthe high brightness of the skull area, the regions whose grayscalevalues are greater than a pre-set threshold may be selected in theselected section images as the candidate regions of the skull. Theselected section images may be the median sagittal section image and/orat least one section image which are parallel to the median sagittalsection image. The threshold may be determined according to the actualneeds. For example, the threshold may be an experience threshold, or mayalso be determined according to the statistical characteristics of theimage. For example, the threshold may be set as equal to the mean ofgray value multiplied by an experience coefficient. In anotherembodiment, based on the characteristics that the skull area is brightin middle and dark at both sides, operators may be designed based ondifferential. The convolution of the operators with the selected sectionimage may be performed, and then the portion whose convolution valuesare greater than a pre-set threshold may be retained as thecharacteristics image. The operators designed based on differential maybe one or more of the following operators:

$\begin{matrix}\begin{bmatrix}{- 1} & {- 1} & {- 1} \\2 & 2 & 2 \\{- 1} & {- 1} & {- 1}\end{bmatrix} & (1)\end{matrix}$ $\begin{matrix}\begin{bmatrix}{- 1} & \ldots & {- 1} \\ & 0 & \\2 & \ldots & 2 \\ & 0 & \\{- 1} & \ldots & {- 1}\end{bmatrix} & (2)\end{matrix}$ $\begin{matrix}\begin{bmatrix}{- 1} \\0 \\\ldots \\0 \\2 \\0 \\\ldots \\0 \\{- 1}\end{bmatrix} & (3)\end{matrix}$ $\begin{matrix}\begin{bmatrix}1 & 2 & 1 \\0 & 0 & 0 \\{- 1} & {- 2} & {- 1}\end{bmatrix} & (4)\end{matrix}$ $\begin{matrix}\begin{bmatrix}{- 1} & {- 2} & {- 1} \\0 & 0 & 0 \\1 & 2 & 1\end{bmatrix} & (5)\end{matrix}$

After the characteristics extraction, generally multiple connectedcharacteristics regions may be retained in the characteristics image.Therefore, one or more connected characteristics regions may be retainedas the candidate regions based on certain rules. For example, the rulesmay be that the one or more characteristics regions of which the sum ofthe characteristics values are maximum are selected as the candidateregions. The characteristics values may be the characteristics used forextracting the skull characteristics above, such as the gray valuecharacteristics. The characteristics values may also be thecharacteristics commonly used in characteristics extraction in digitalimage processing, such as the texture characteristics. In oneembodiment, the rules may be that one or more characteristics regions ofwhich the average characteristics values are maximal are selected as thecandidate regions. In another embodiment, machine learning methods maybe used, in which the characteristics may be extracted from thecharacteristics regions, the extracted characteristics may be inputtedinto a pre-trained classifier to be classified, and the candidateregions may be determined based on the classification results. Theextracted characteristics herein may be the gray value averages of thecharacteristics regions, the characteristics value averages, thecurvatures of the characteristics regions, entropy of thecharacteristics regions, the first moment or the second moment, etc. Thepre-trained classifier may be obtained by extracting the characteristicsmentioned above from a certain number of samples and performing thetraining using PCA (Principal Component Analysis), KPCA (KernelPrincipal Component Analysis), ICA (Independent Component Analysis), LDA(Linear Discriminant Analysis), SVM (Support Vector Machine) or otherclassifier. The implementation of the classifier may be similar to thosein image processing and pattern recognition techniques and will not bedescribed in detail here. It shall be understood that, in the case thatthe number of the selected section images is greater than 1 (i.e.multiple section images are selected) the definition of the rules may besimilar. For example, one or more characteristics regions whose sums ofcharacteristics values or the characteristics value averages are maximummay be selected as the candidate regions of the skull, or the machinelearning methods may be used to identify the candidate regions.

In step 254, in the candidate regions, the bending direction of theskull may be used to determine the orientation of the skull. Manymethods may be used to determine the bending direction of the skull. Forexample, a quadratic curve may be used to fit the connected regions, andthe orientation of the skull may be determined according to thecoefficient of the quadratic term of the quadratic curve. For example,in the case that the coefficient of the quadratic term is greater than0, the skull is towards the bottom, on the contrary, the skull istowards the top. In another embodiment, machine learning methods may beused, which may be obtained by training using PCA, KPCA, LDA, SVM orother methods. In the case of multiple candidate connected regions, theorientation of each connected region may be respectively determined andthe final orientation of the skull may be determined by vote.

After the orientation of the fetal head is determined, in the case thatthe fetal head is towards the bottom, the median sagittal section imagemay be rotated by 180 degree or be upside down and then be displayed,such that the displayed image is more suitable for observation, therebyfacilitating the observation of the doctor to the fetus.

In the embodiments above, the plane equation is used to express theresults of the extraction for the convenience of description. However,the present disclosure is not limited to the plane equation. Othermethods mentioned above or in the art may also be used. The expressionof the results of the sagittal section image only differ in form, butwill not affect the essence of the present disclosure.

In some embodiments, the three-dimensional ultrasound imaging systemimplementing the methods above will not be limited to the ultrasoundimaging system integrated as a single device (for example the cartultrasound imaging system or portable ultrasound imaging system), butmay also be a distributed system. For example, at least a portion of thesteps or functions of the methods above may be implemented in otherdevice which is connected (wired or wirelessly) to the cart ultrasoundimaging system or portable ultrasound imaging system through datacommunication device. Said other device may be, for example, dataprocessing workstation, personal computer, various smart portabledevices, other ultrasound imaging device or various network server, etc.Therefore, the three-dimensional ultrasound imaging system in thepresent disclosure may be formed by the device implementing the at leasta portion of the steps or functions and the cart ultrasound imagingsystem or the portable ultrasound imaging system.

With the ultrasound imaging methods of the embodiments above, ultrasoundscanning may be performed on the fetus to obtain the three-dimensionalvolume data of the fetal head, and the median sagittal section image ofthe fetal head may be automatically extracted according to the obtainedthree-dimensional volume data and the orientation of the fetal head maybe automatically determined (for example, determining whether the fetusis upside down). In the case that the fetus is upside down, the mediansagittal section image may be rotated such that it is upright.Therefore, the displayed median sagittal section image will be suitablefor observation of human, and the issue that it is difficult for thedoctor to accurately find out the median sagittal section image manuallymay be solved. Accordingly, the doctor can conveniently observe themedian sagittal section image of the fetal head.

The three-dimensional ultrasound imaging methods or systems provided bythe present embodiment may be similar to those in the embodiments above.The difference is that, in step 25 of the embodiments above, theorientation of the fetal head in the median sagittal section image maybe determined according to the orientation of the skull, while in step25 of the present embodiment, the orientation of the fetal head in themedian sagittal section image may be determined according to theorientation of the cavum septi pellucid.

As shown in FIG. 5 and FIG. 8 , in the ultrasound image, the shape ofthe cavum septi pellucid is crescent. When the fetus in the ultrasoundimage is upright, the cavum septi pellucid is represented as an upwardlyconvex crescent. On the contrary, when the fetus in the ultrasound imageis upside down, the cavum septi pellucid is represented as a downwardlyconvex crescent. Therefore, the orientation of the cavum septi pellucidmay be used to determine the orientation of the fetal head in the mediansagittal section image.

In the present embodiment, the process of determining the orientation ofthe fetal head in the median sagittal section image according to theorientation of the cavum septi pellucid may include step 252′ and step254′, as shown in FIG. 11 .

In step 252′, the connected region corresponding to the cavum septipellucid may be detected from the median sagittal section imageaccording to the characteristics of the cavum septi pellucid inultrasound image.

In step 252′, since the cavum septi pellucid is represented as dark areawhile its surrounding areas generally have higher brightness in theultrasound image, multiple methods may be designed based on suchcharacteristics to segment the dark area, thereby detecting the cavumsepti pellucid. For example, image segmentation algorithms may be usedto segment the region which may be considered as the cavum septipellucid, such as threshold segmentation, Snake algorithm, level-setalgorithm or graph-cut segmentation, etc. Generally, multiple regionsmay be obtained by the segmentation. Therefore, certain rules may be setto select the region which is most similar to the cavum septi pellucid.For example, it may be determined according to the shape, grayscalevalue, variance or other characteristics, or the combination thereof,thereby obtaining the connected region corresponding to the cavum septipellucid.

In step 254′, the orientation of the cavum septi pellucid may bedetermined based on the connected region and the orientation of the headof the fetus in the median sagittal section image may be determinedbased on the result of the determination.

In step 254′, the methods similar to them methods for determining theorientation of the skull in the embodiments above may be used todetermine the orientation of the cavum septi pellucid based on theconnected region corresponding to the cavum septi pellucid.

For example, methods similar to steps 254 a 1 to 254 a 3 may be used todetermine the orientation of the cavum septi pellucid. First,mathematical morphology processing may be performed on the connectedregion corresponding to the cavum septi pellucid to extract the skeletonof the connected region to obtain a skeleton image. Thereafter, alongest, continuous curve may be searched in the skeleton image, whichmay be a representative curve. The orientation of the cavum septipellucid may be determined based on the coordinates of at least onepoint in the middle and at least one point at the end of the searchedcurve.

In one embodiment, methods similar to steps 254 b 1 to 254 b 2 may beused to determine the orientation of the cavum septi pellucid. First, amiddle line in vertical direction of the connected region correspondingto the cavum septi pellucid may be obtained. Thereafter, the orientationof the cavum septi pellucid may be determined using methods similar tostep 254 a 3.

Based on the determined orientation of the cavum septi pellucid, in thecase that the cavum septi pellucid is upwardly convex, the head of thefetus in the median sagittal section image is towards the top, while inthe case that the cavum septi pellucid is downwardly convex, the head ofthe fetus in the median sagittal section image is towards the bottom. Inthe case that the head is towards the bottom, the median sagittalsection image may be rotated by 180 degree or be upside down, such thatthe displayed image may be more suitable for observation, therebyfacilitating the observation of the doctor to the fetus.

A three-dimensional ultrasound imaging method in one embodiment is shownin FIG. 12 . The method may include obtaining the three-dimensionalvolume data of the fetal head (step 21), extracting the median sagittalsection image (step 23), determining the orientation of the fetal face(step 27) and displaying the median sagittal section image (step 29).The steps 21, 23 and 29 may be similar to those in the embodiments aboveand will not be described herein. In the present embodiment, athree-dimensional ultrasound imaging device which implements the methodof the present embodiment may also be provided. Except thethree-dimensional imaging unit, this three-dimensional ultrasoundimaging device may be similar to that in the embodiments above and willnot be described in detail again herein.

Since the cavum septi pellucid is located in the front of the brain, theface and back of the fetus (i.e., the orientation of the fetal face) maybe determined according to the location of the cavum septi pellucid.

In the present embodiment, the flow chart of step 27 may be as shown inFIG. 13 , which may include detecting the cavum septi pellucid (step271), determining the intracranial center (step 272) and determining thelocation of the cavum septi pellucid (step 273).

In step 271, the cavum septi pellucid may be detected using a methodsimilar to those for detecting the cavum septi pellucid in theembodiments above. For example, an image segmentation algorithm may beperformed on the median sagittal section image to obtain at least oneregion, and the region which is most similar to the cavum septi pellucidin region characteristics may be selected as the cavum septi pellucidregion. The specific processes may be similar to step 252′ above andwill not be described in details again.

In step 272, the intracranial center may be determined according to thefetal skull in the volume data. In the volume data, the fetal skull maybe represented as an approximate ellipsoid or ellipse, and berepresented as high brightness region in ultrasound image. Therefore, inone embodiment, the determination of the intracranial center may includesteps 272 a 1 to 272 a 3.

In step 272 a 1, at least one frame of A section image (i.e., horizontalsection image obtained along A-A in FIG. 4 ) may be selected from thevolume data.

In step 272 a 2, operators may be designed based on differential, suchas one or more of the operators (1) to (5) above, and characteristicsextraction may be performed on the A section image using the designedoperator.

In step 272 a 3, ellipsoid or ellipse detection may be performed on theextracted characteristics. Any ellipse detection method may be used,such as least squares estimation method, Hough transform method orrandom Hough transform method, etc. For example, The Hough transformmethod described below may be used to detect the ellipsoid or ellipse.

After the ellipse or the ellipsoid id detected, the center of theellipse or the ellipsoid may be determined according to relatedgeometric knowledge.

In step 273, the coordinate of the region of the cavum septi pellucidmay be compared with the coordinate of the center, and the orientationof the fetal face in the median sagittal section image may be determinedbased on the result of the comparison. Specifically, the location of acertain point (whose coordinate on the X axis may be x) in the region ofthe the cavum septi pellucid may be selected as the location of thecavum septi pellucid, such as the central location of the cavum septipellucid region detected in step 271 or other location. Assuming thatthe coordinate of the center of the ellipse or ellipsoid obtained instep 272 in X axis is xcenter, x may be compared with xcenter. In thecase of x<xcenter, it is indicated that the cavum septi pellucid islocated on the left of the intracranial center in the median sagittalsection image, i.e., the front portion (face) of the fetus is located atthe left portion of the median sagittal section image while the rearportion (back) of the fetus is locate at the right portion. In theopposite case, the rear portion (back) of the fetus is located at theleft portion of the median sagittal section image while the frontportion (face) of the fetus is locate at the right portion.

Based on the determined orientation of the fetal head (e.g., theorientation of the face or the top of the head, etc.) in the mediansagittal section image, the front portion and/or the rear portion of thefetus may be marked on the median sagittal section image, as shown inthe view on the right of FIG. 8 . Alternatively, in one embodiment, afetal head icon may be used to indicate the orientation of the frontportion/real portion or the top of the head or the face of the fetus, asshown in FIGS. 25 and 26 , where the orientation of the face of the iconmay represent the orientation of the face in current image. The mediansagittal section image in which the orientation of the fetal head (e.g.,the front portion and/or real portion of the fetus, etc.) is marked maybe display on the display, thereby facilitating the observation of thedoctor to the fetus.

FIG. 14 schematically shows a flow chart of a three-dimensionalultrasound imaging method in one embodiment. The steps of this methodmay be similar to those in the embodiments above and will not bedescribed in detail again. It should be understood that the order ofstep 25 and step 27 can be reversed, i.e., the orientation of the facemay be determined first, and then the orientation of the head may bedetermined. In the present embodiment, the orientation of the fetal headand the fetal face may be determined, such that the doctor can moreeasily identify the tissues in the sagittal section image.

The processes for extracting the median sagittal section image in step23 in some embodiments will be described below.

In one embodiment, the flow chart of extracting the median sagittalsection image from the three-dimensional volume data may be as shown inFIG. 15 , which may include extracting characteristics (step 80),selecting characteristic points (step 81) and detecting a planedetermined by the selected characteristic points (step 82).

As mentioned above, the median sagittal section image will have largergray value than surrounding areas in the three-dimensional volume data.In one embodiment, this characteristic may be used to extract the mediansagittal section image from the three-dimensional volume data.Therefore, in step 80, sagittal section characteristic regions whichrepresent plane regions having larger gray values than the areas locatedoutside both sides thereof may be extracted from the three-dimensionalvolume data. In other words, some characteristic regions may beextracted from the three-dimensional volume data. The characteristicregions may represent plane regions which have larger gray value thanthe areas located outside both sides of these plane regions. Theseextracted characteristic regions may be the sagittal sectioncharacteristic regions mentioned above. This way, the characteristic of“the median sagittal section image having larger gray value thansurrounding areas” may be fully utilized to facilitate the extraction ofthe median sagittal section image.

A variety of suitable ways may be used to extract the sagittal sectioncharacteristic regions from the three-dimensional volume data. Forexample, in one embodiment, convolution may be performed on thethree-dimensional volume data using a characteristic extraction operatorto obtain a convolution image, which will contain the sagittal sectioncharacteristic regions extracted.

In one embodiment, the convolution may be performed on each frame imageof the three-dimensional volume data using two-dimensionalcharacteristic extraction operator, respectively, and then the obtainedconvolution images may be combined to form a three-dimensionalconvolution volume data. Alternatively, the convolution may also beperformed directly on the three-dimensional volume data using athree-dimensional characteristic extraction operator designed therefor.The specific steps of the convolution may be well known in the art andthus will not be described in detail herein.

In the embodiments, the characteristic extraction operators may bedesigned according to the image characteristics to be extracted. Forexample, in the embodiments mentioned above, the sagittal sectioncharacteristic regions having larger gray value than the area outsideboth sides thereof need to be extracted. In this case, one or more ofthe characteristic extraction operators (1) to (5) in the embodimentabove may be used.

In one embodiment, a characteristic extraction operator obtained bytransposition (matrix transposition), rotation or combination of thecharacteristic extraction operators mentioned above may also be used.Alternatively, other suitable characteristic extraction operators, suchas the Roberts operator, the Laplace Gauss operator or the modificationthereof or the like, may also be used.

In one embodiment, a three-dimensional characteristic extractionoperator may also be used.

In one embodiment, the size of the characteristic extraction operator(two-dimensional or three-dimensional) may be set as required.

After the sagittal section characteristic regions are extracted in thestep 80, characteristic points which satisfy certain conditions may beselected from the extracted sagittal section characteristic regions instep 81. In general, at least three characteristic points may beselected. The characteristic point parameters of the selectedcharacteristic points may be recorded, which will be used in followingsteps.

In one embodiment, the “characteristic point parameters” may include thecoordinates and/or the value (for example, the gray value or the resultvalue of convolution, etc.) of the characteristic points.

In one embodiment, the “certain conditions” mentioned above may bedetermined according to the properties of the characteristic extractionoperator used. For example, when the characteristic extraction operators(1)-(5) mentioned above are used, the certain conditions may be that theresult value of convolution of the point is larger than a certainthreshold value. The threshold value may be an empirical parameter andmay be determined as required.

In one embodiment, in order to facilitate the following plane detectionstep (described in details below) and reduce the impact of noise, thepoints which are obviously not within the head may be excluded accordingto certain prior knowledge. For example, the head usually is located atthe center of the three-dimensional volume data. Therefore, thecharacteristic points may be selected from points which are locatedwithin a sphere or ellipsoid centered at the center of thethree-dimensional volume data and having a radius of a threshold value.This threshold value may also be an empirical parameter or determined asrequired.

The characteristic points selected in the step 81 generally candetermine a plane. In one embodiment, this plane may be the plane of themedian sagittal section and the section image in the three-dimensionalvolume data which is coincide with this plane may be the median sagittalsection image of the head of the fetus. Therefore, in one embodiment,the plane of the median sagittal section of the head of the fetus may bedetermined by acquiring the plane determined by the selectedcharacteristic points.

The plane determined by a plurality of characteristic points may beacquired by various methods, such as weighted Hough transformation,random Hough transformation, least squares estimation, or Radontransformation, etc.

For example, in one embodiment, the weighted Hough transformation may beused to acquire the plane determined by the selected characteristicpoints, which will be described in details below.

In a three-dimensional space, a plane generally can be expressed asaX+bY+cZ+d=0 or Z=aX+bY+C or Y=aX+bZ+c, where a, b, c and d are theplane parameters which determine the plane.

In a three-dimensional space, the plane may also be expressed asfollowing formula:ρ=cos θ cos φX+sin θ cos φY+sin φZ  (6),

where θ, φ and ρ are the plane parameters and their meaning may be thoseas shown in FIG. 16 . One set of θ, φ and ρ may determine one plane.

The plane parameters θ, φ and ρ in the formula (6) have respectiveranges which are related to the way for defining the three-dimensionalCartesian coordinate system. For example, in a three-dimensional volumedata, when the position of the origin of the three-dimensional Cartesiancoordinate system vary, the ranges of the plane parameters will varycorrespondingly.

For example, in the embodiment shown in FIG. 16 , the range of theparameter ρ may be as following:0≤ρ≤√{square root over ((W−1)²+(H−1)²+(F−1)²)}  (7).

where W, H and F are the sizes of the three-dimensional volume data, Fis the number of the frame images in the three-dimensional volume data,W is the width of the frame image, and H is the height of the frameimage.

It will be understood that when the three-dimensional Cartesiancoordinate system is defined in other ways, the ranges of the planeparameters θ, φ and ρ will be different.

In the three-dimensional space corresponding to the three-dimensionalvolume data, there are infinite number of planes which pass through onecertain point, i.e., there are infinite number of sets of θ, φ and ρ,which will form a parameter space (which may be referred to as θ-φ-ρspace), i.e., a Hough space. The concept of the Hough transformation isprojecting the points in the three-dimensional space corresponding tothe three-dimensional volume data into the Hough space and detecting thepeak value of the Hough space. The peak value corresponds to the planein the three-dimensional space corresponding to the three-dimensionalvolume data.

In one embodiment, θ, φ and ρ are continuous. Therefore, θ, φ and ρ maybe sampled to be divided into a plurality of units (as shown in FIG. 17). Accordingly, the weighted Hough transformation may include steps S11to S14 as shown in FIG. 18 .

S11: calculating ranges and sampling steps of the plane parameters. Therange of the parameter ρ may be as shown in formula (7). The largestranges of the parameter θ and φ may be determined with reference to FIG.16 , such as, 0°≤θ<360° and −90°≤φ≤90°.

In one embodiment, the ranges may also be narrowed according to certainprior knowledge.

The ranges calculated may be expressed as θ_(min)≤θ≤θ_(max),φ_(min)≤φ≤φ_(max) and ρ_(min)≤ρ≤ρ_(max). The sampling steps θstep, φstepand ρstep may be determined according to the extraction accuracyrequired. For example, in one embodiment, θ_(step)=1, φ_(step)=1,ρ_(step)=2. In other embodiments, other suitable values may also beused.

S12: generating and initializing the Hough matrix. The Hough matrix maybe generated and be initialized to zero. The size of a three-dimensionalHough matrix may be:

$\begin{matrix}{\frac{\left( {\theta_{{ma}x} - \theta_{min}} \right)}{\theta_{step}} \times \frac{\left( {\varphi_{m{ax}} - \varphi_{min}} \right)}{\varphi_{step}} \times {\frac{\left( {\rho_{{ma}x} - \rho_{min}} \right)}{\rho_{step}}.}} & (8)\end{matrix}$

In one embodiment, three one-dimensional Hough matrixes may also beused, the sizes of which may be

$\frac{\left( {\theta_{{ma}x} - \theta_{min}} \right)}{\theta_{step}},{\frac{\left( {\varphi_{m{ax}} - \varphi_{min}} \right)}{\varphi_{step}}{and}\frac{\left( {\rho_{{ma}x} - \rho_{min}} \right)}{\rho_{step}}},$respectively.

S13: Voting the parameters. For example, a voting value ρl may becalculated as following for each of the selected characteristic pointsand each θ_(j) and φ_(k) in the range aforementioned:ρ_(l)=cos θ_(j) cos φ_(k) X _(i)+sin θ_(j) cos φ_(k) Y _(i)+sin φ_(k) Z_(i)  (9).

where (X_(i), Y_(i), Z_(i)) are the coordinates of the i^(th)characteristic point Pi.

Then the Hough matrix may be updated as:H(θ_(j),φ_(k),ρ_(l))=H(θ_(j),φ_(k),ρ_(l))+Vi  (10).

where Vi is the value of the i^(th) characteristic point Pi (forexample, the gray value or the result value of convolution, etc.).

S14: detecting peak value of the Hough matrix. The θ, φ and ρcorresponding to the peak value of the Hough matrix H may be calculated.Provided that the location of the peak value of the Hough matrix H is(θ_(j), φ_(k), ρ_(l)), the plane acquired may be:θ=θ_(j)θ_(step)+θ_(min)φ=φ_(k)φ_(step)+φ_(min)ρ=ρ_(l)ρ_(step)+ρ_(min)  (11).

In the embodiments aforementioned which use three one-dimensional Houghmatrixes, the θ, φ and ρ corresponding to the peak values of the Houghmatrixes may be calculated respectively.

In these embodiments, the weighted Hough transformation takes intoaccount the difference between the contributions of respectivecharacteristic points Pi to the plane acquisition. The larger its valueVi, the more it contribute to the Hough matrix.

In one embodiment, the difference between the contributions ofrespective characteristic points may also not be taken into account.That is, the value Vi of every characteristic point may be set as 1. Inthis case, the plane determined by these characteristic points may alsobe acquired. In fact, in this case, the weighted Hough transformationaforementioned is degenerated into a traditional Hough transformation.

In one embodiment, other methods for plane acquisition may also be used.For example, in one embodiment, the plane determined by the selectedcharacteristic points may be acquired by random Hough transformation,which may include steps S21 to S27, as shown in FIG. 19 .

S21: calculating ranges and sampling steps of the plane parameters. Inthis step, the ranges and the sampling steps of the plane parameters θ,φ and ρ may be calculated. This step may be the same as or similar tothe step S11 aforementioned.

S22: generating a Hough matrix and initializing it to zero. In thisstep, a three-dimensional Hough matrix may be generated and beinitialized to 0. This step may be the same as or similar to the stepS12 aforementioned.

S23: selecting points randomly. In this step, three points may beselected randomly from the selected characteristic points.

S24: solving the plane equation to acquire the plane parameters. In thisstep, the coordinates of the three points may be substituted into theplane equation to solve for the plane parameters θ, φ and ρ. Thespecific methods for solving for plane parameters are well known in theart and will not described in details herein.

S25: updating the Hough matrix. In this step, the values at thelocations corresponding to the θ, φ and ρ in the Hough matrix may beincreased by 1.

S26: repeating the steps S23 to S25 for N times. N herein may be apredefined parameter, and may be set as required. For example, in oneembodiment, N may be 50000. In other embodiments, N may be other value.

S27: detecting the peak value of the Hough matrix. In this step, thelocation in the Hough matrix which has maximum value may be acquired andthe θ, φ and ρ corresponding to the location represent the planeacquired.

In one embodiment, another method (which is referred to as stochasticoptimal energy method herein) for acquiring the plane determined by theselected characteristic points may include steps S31 to S37 as shown inFIG. 20 .

S31: initializing an optimal energy E_best=0.

S32: selecting points randomly. In this step, three points may beselected randomly from the selected characteristic points.

S33: solving equations. In this step, the coordinates of the threepoints may be substituted into plane equations to solve for the planeparameters θ, φ and ρ.

S34: calculating current energy E. In this step, an “energy” E of theselected characteristic points from which the distances to the planeacquired in step S33 are less than ε may be calculated.

In this step, the distance from each characteristic point Pi of theselected characteristic points to the plane (θ, φ, ρ) acquired in stepS33 may be calculated. When the distance is less than ε, the value Vi ofthe current characteristic point may be accumulated to the energy E,i.e., E=E+Vi. ε is a parameter which may be set as required. Forexample, in one embodiment, ε may be set as 5. ε may also be set asother value.

S35: updating the energy. If the current energy E>E_best, E_best may beset as E and the current plane parameters may be updated as the optimalplane parameters. Otherwise, it turns to step S36.

S36: repeating steps S32 to S35 for N times. N is iteration time and maybe set as required.

S37: outputting the plane parameters. After the step S36 is completed,the plane parameters corresponding to the iteration with maximum energymay be outputted as acquired plane parameters.

In this way, one plane determined by the select characteristic pointsmay be acquired.

In one embodiment, in the step S34, the value Vi of the currentcharacteristic point may also not be accumulated. Rather, when thedistance from point Pi to the plane is less than ε, E=E+1. That is, thecontributions of the selected characteristic points to the planeacquisition are considered as to be the same.

In the embodiments above, the plane expression in formula (6) is used.The plane is acquired by calculating the coefficients 6, cps and p ofthe formula. However, the form of the plane expression does not affectthe methods of the embodiments. The methods described above are alsosuitable for other plane expression such as aX+bY+cZ+d=0, Z=aX+bY+c orY=aX+bZ+c after simple modification.

After the median sagittal section image of the fetal head is extractedusing the methods of the embodiments above, the orientation of the fetalhead and/or the fetal face may be determined with reference to theembodiments above. In the case that the head is upside down, the mediansagittal section image may be adjusted such that the head is uprightand/or the orientation of the face may be marked in the median sagittalsection image. Thereafter the adjusted and/or marked median sagittalsection image may be displayed in order to facilitate the observation ofthe doctor to the fetal head.

As described above and with further reference to FIG. 4 to FIG. 7 , inthe three-dimensional image of the head of the fetus, the mediansagittal section is the longitudinal section located at the middle ofthe head of the fetus, and other section images which intersect with themedian sagittal section image will contain information at theintersection position of said other section images with the mediansagittal section image, in other words, will contain information at theintersection line. In said other section image, the intersection line ofsaid other section image with the median sagittal section image appearas a line with higher brightness (because, as described above, in thethree-dimensional image or the three-dimensional volume data of a fetalhead, the median sagittal section image has larger brightness thansurrounding areas), i.e., the brain midline. The collection of the brainmidlines forms the median sagittal section image. Therefore, in someembodiments of the present disclosure, these characteristics may be usedto acquire the median sagittal section image from the three-dimensionalvolume data.

For example, in one embodiment, a schematic flow chart of extracting themedian sagittal section image from the three-dimensional volume data maybe as shown in FIG. 21 , which may include extracting at least twosection images from the three-dimensional volume data (step 110),extracting brain midlines from the section images (step 111) anddetecting the plane determined by the brain midlines (step 112).

In one embodiment, in step 110, at least two section images may beextracted from the three-dimensional volume data. The section images maybe extracted in different ways. For example, the section images whichare parallel to the section L2 in FIG. 5 and/or parallel to the sectionL1 in FIG. 5 may be extracted. Alternatively, any other suitable sectionimages may be extracted, such as the section images which are at certainangles with respect to the section L2 and/or the section L1. The numberof the extracted section images may also not be limited.

After the section images are extracted, in step 111, the brain midlinemay be extracted from each of the section images, thereby obtainingmultiple straight lines representing the brain midline.

A brain midline appears as a straight line in a section image which haslarger gray value than the areas outside both sides thereof. Therefore,the extraction of the brain midline may be achieved based on thischaracteristic.

In one embodiment, extracting the brain midline from each of theextracted section image may include steps S40 to S41 as shown in FIG. 22.

S40: extracting brain midline characteristic regions.

In one embodiment, the brain midline characteristic regions which matchthe characteristics of the brain midline aforementioned, i.e., the brainmidline characteristic regions which represent lines having larger grayvalue than areas outside both sides thereof, may be extracted from saidsection image. The methods for extracting the brain midlinecharacteristic regions may be similar to the methods for extracting thesagittal section characteristic regions aforementioned. For example,convolution may be performed on said section image using characteristicextraction operators. The section image processed by the convolutioncontains the extracted brain midline characteristic regions.

It should be understood that the “line” and the “brain midline”mentioned herein should not be ideally interpreted as theoretical lines,but regions having certain width and/or thickness.

The characteristics extraction operator may be designed based on thecharacteristics of the brain midline to be extracted. In theembodiments, the characteristics of the brain midline are similar to thecharacteristics of the median sagittal section described above.Therefore, the characteristic extractions operator which is similar tothe characteristic extraction operator described above, such as similarto any one of the operators (1) to (5), may be used.

After the brain midline characteristic regions are extracted, at leasttwo characteristic points which satisfy certain conditions may beselected from the brain midline characteristic regions, andcharacteristic point parameters of the at least two characteristicpoints may be recorded. The characteristic point parameters of thecharacteristic point may include the coordinates of the characteristicpoint and/or the value of the characteristic point (for example, thegray value or the result value of convolution) or other suitableparameters.

The certain conditions mentioned herein may be determined based on theproperties of the characteristic extraction operator used. For example,when the operators similar to the characteristic extraction operators(1) to (5) above are used, the certain condition may be set as that theresult value of convolution of the point is greater than a threshold.The threshold may be an empirical parameter and may be determined asrequired.

S41: detecting straight lines.

The selected characteristic points generally determine straight lines.In one embodiment, the straight line determined by the selectedcharacteristic points may be acquired, which represents the brainmidline in the section image.

The weight Hough transformation, the random Hough transformation and thestochastic optimal energy method described above may also be suitablefor acquiring the straight lines in the present step after simplemodification in details.

For example, the standard equation of a straight line may be ρ=cosθX+sin θY, which has two parameters θ and ρ. Compared with the planeequation, there is no the parameter φ. When the weighted Houghtransformation or the random Hough transformation is used, the Houghmatrix is a two-dimensional ρ_θ matrix. When the random Houghtransformation or the stochastic optimal energy method is used, twopoints may be randomly selected from the selected characteristic pointsfor each iteration, which may be enough to acquire a straight line. Theother parts of the methods for acquiring the straight line may besubstantially similar to the methods for acquiring the plane describedabove and will not be described in details herein.

In one embodiment, other methods may also be used to acquire thestraight line determined by the selected characteristic points, forexample, including, but not limited to, random transformation, phaseencoding or least square estimation, etc.

Based on the characteristics of the median sagittal section image in thethree-dimensional image of a fetal head, the brain midline straightlines acquired will determine a plane. This plane determined by thebrain midline straight lines may be the plane on which the mediansagittal section image is located.

Therefore, after the brain midline straight lines in the extractedsection images are acquired in the step 111, the plane determined by thebrain midline straight lines may be acquired in step 112. In this way,the plane on which the median sagittal section is located, i.e., theplane on which the median sagittal section image of the head of thefetus is located, may be acquired.

Various methods may be used to acquire the plane determined by the brainmidline straight lines. For example, in some embodiments, three pointswhich are not collinear may be selected from the acquired brain midlinestraight lines. The coordinates of the three points may be substitutedinto the plane equation to calculate the plane parameters. In otherembodiments, these steps may be performed for several times and theaverage of the results of the acquisition will be the final acquisitionresult.

Another method may also be used. N points may be selected from theacquired brain midline straight lines and the plane parameters may befitted using least square estimation. In other embodiments, the N pointsselected may serve as inputs and the plane parameters may be acquiredusing the Hough transformation, the random Hough transformation, thestochastic optimal energy method or the like which are similar to thosedescribed above.

After the median sagittal section image of the fetal head is extractedusing the methods of the embodiments above, the orientation of the fetalhead and/or the fetal face may be determined with reference to theembodiments above. Based on the determined orientation, the mediansagittal section image in which the fetal head is upside down may beadjusted such that the head is upright and/or the orientation of theface may be marked in the median sagittal section image. Thereafter theadjusted and/or marked median sagittal section image may be displayed.

As described above and with further reference to FIG. 4 , the tissuestructures outside both sides of the median sagittal section in a fetalhead are approximate symmetrical with respect to the median sagittalsection, therefore the image data of the three-dimensional image of afetal head outside both sides of the median sagittal section image willhave approximate symmetry with respect to the median sagittal sectionimage. In one embodiment, this characteristic may be used to acquire themedian sagittal section image from the three-dimensional volume data.For example, a plurality of candidate section images may be selectedfrom the three-dimensional volume data and the symmetry of the areasoutside both sides of the candidate section images may be calculated.The candidate section image with best symmetry of areas outside bothsides thereof may be the median sagittal section image desired.

For example, in one embodiment, a schematic flow chart of extracting themedian sagittal section image from the three-dimensional volume data maybe as shown in FIG. 23 , which may include selecting candidate sectionimage (step 120), calculating symmetry indicators of the candidatesection images (step 121) and determining the section imagecorresponding to the symmetry indicator which satisfy a certaincondition (step 122).

In step 120, a group of candidate section images may be selected fromthe three-dimensional volume data. The candidate section images may beselected as required. For example, in one embodiment, all section imagesin a certain range in the three-dimensional volume data which are acertain spacing (or step) apart from each other in one or more certaindirections may be selected as the candidate section images. Herein, the“certain range” may be an angle range with respect to one or more linesand/or planes in the three-dimensional volume data. Alternatively, the“certain range” may also be a distance range with respect to one or morepoints, lines and/or planes in the three-dimensional volume data. The“in one or more certain directions” may mean that the normal line of thesection image is in said one or more certain directions. The “spacing”or “step” may be a distance spacing or step, or a angle spacing or step.

In one embodiment, all section images which are certain spacing or stepapart from each other in one or more certain directions in the whole ofthe three-dimensional volume data may be selected. Alternatively, in oneembodiment, some prior knowledge may be used to assist the selection ofthe candidate section images to exclude the section images for which itis impossible to be the median sagittal section image. For example,since the median sagittal section of the head of the fetus is alongitudinal section (i.e., a section in the direction from the top tothe neck of the fetus) which is located at the center position of thehead of the fetus, the longitudinal section images which are locatedsubstantially at the center position of the head may be selected as thecandidate section images based on the direction of the fetus image inthe three-dimensional volume data. In the present disclosure, thesection images in the direction from the top portion to the neck portionof the fetus in all, or at least a part, of the three-dimensional volumedata (in other words, the section images which are substantiallyparallel to the direction from the top portion to the neck portion ofthe fetus, or the section images whose normal lines are substantiallyperpendicular to the direction from the top portion to the neck portionof the fetus) may be referred to as “longitudinal section image” of thethree-dimensional volume data.

Therefore, in one embodiment, a group of longitudinal section images inthe three-dimensional volume data may be selected as the candidatesection images. For example, a group of longitudinal section imageswhich are located at the center position of the head of the fetus (forexample, all longitudinal section images which are a certain spacing orstep apart from each other in a certain region at the center of thehead) may be selected as the candidate section images.

Alternatively, in one embodiment, user inputs which indicate thepossible range of the median sagittal section image may be received.Thereafter, the section images in the range indicated by the user may beselected as the candidate section images

In one embodiment, all section images which are a certain step apartfrom each other in the whole of the three-dimensional volume data may beselected as the candidate section images. That is, all section imageswithin the whole three-dimensional volume data may be searched using acertain step

For example, in one embodiment, when the plane equation in the formula(6) is used, the candidate section images may be selected by determiningthe ranges of the plane parameters θ, φ and ρ and the values of thesteps θstep, φstep and ρstep.

Similarly, when the plane equation aX+bY+cZ+d=0 Z=aX+bY+c or Y=aX+bZ+cis used, the candidate section images may be selected by determining theranges of a, b, c and d and their steps.

For example, when all section images which are a certain step apart fromeach other in the whole of the three-dimensional volume data areselected as the candidate section images, the rang of ρ may be as shownin the formula (7) and the largest ranges of θ and φ may be, forexample, 0°≤θ<360° and −90°≤φ≤90° (with reference to FIG. 16 ). It willbe understood that when the coordinate system is defined in differentway the ranges of the parameters will be different.

The steps θstep, φstep and ρstep may be determined based on theacquisition accuracy required and are not limited by the presentdisclosure. For example, in one embodiment, θ_(step)=1, φ_(step)=1,ρ_(step)=2. It will be understood that the steps may also be othervalues based on the acquisition accuracy required.

After the candidate section images are selected, in step 121, a symmetryindicator may be calculated for each candidate section image (ρ, θ, φ).

The symmetry indicator may be mainly used to measure the similarity ofthe data located outside both sides of the candidate section image.

For example, in one embodiment, for each candidate section image, atleast one pair of first region and second region, which are locatedoutside both sides of the candidate section image in thethree-dimensional volume data, may be selected. The first region and thesecond region are symmetrical with respect to the candidate sectionimage. Then the data in the first region and the data in the secondregion may be used to calculate the symmetry indicator of the candidatesection image

Herein, the “data in the first region” may refer to the values of thedata points of the three-dimensional volume data which are located inthe first region. Similarly, the “data in the second region” may referto the values of the data points of the three-dimensional volume datawhich are located in the second region.

In one embodiment, for each candidate section image, a plurality ofpairs of first region and second region may be selected. A symmetryindicator may be calculated for each pair of first region and secondregion. Therefore a plurality of symmetry indicators may be obtained.Thereafter, the symmetry indicator of the candidate section image may beobtained based on the plurality of symmetry indicators. For example, thesymmetry indicator of the candidate section image may be the average ofthe plurality of symmetry indicators. Alternatively, the symmetryindicator of the candidate section image may be the weighted average ofthe plurality of symmetry indicators, where the weighted coefficientsmay be determined based on the location of the selected pair of firstregion and second region and other factors. In one embodiment, the finalsymmetry indicator of the candidate section image may be a function ofthe plurality of symmetry indicators.

The symmetry indicator may be calculated by various ways.

For example, in one embodiment, the symmetry indicator may be the sum ofthe absolute value of the difference between the gray values of thecorresponding points in the first region and in the second region, i.e.,

$\begin{matrix}{E = {\sum\limits_{I_{L},{I_{R} \in \Omega}}{❘{I_{L} - I_{R}}❘}}} & (12)\end{matrix}$

where E is the symmetry indicator, Ω is the first region and the secondregion, I_(L) is the data value of the point in the first region, andI_(R) is the data value of the point in the second region which issymmetrical with the point in the first region with respect to thecandidate section image. The “corresponding points in the first regionand in the second region” mentioned above may refer to the points in thefirst region and in the second region which are symmetrical with respectto the candidate section image.

In one embodiment, the symmetry indicator of the candidate section imagemay also be the correlation coefficient between the first region and thesecond region, i.e.,

$\begin{matrix}{E = \frac{\sum\limits_{I_{L},{I_{R} \in \Omega}}{I_{L}I_{R}}}{\sqrt{\sum\limits_{I_{L} \in \Omega}I_{L}^{2}}\sqrt{\sum\limits_{I_{R} \in \Omega}I_{R}^{2}}}} & (13)\end{matrix}$

where E is the symmetry indicator, Ω is the first region and the secondregion, I_(L) is the data value of the point in the first region, andI_(R) is the data value of the point in the second region which issymmetrical with the point in the first region with respect to thecandidate section image.

The symmetry indicator may be defined as described above, but notlimited to. Other definitions may also be used. For example, thesymmetry indicator may also be the Euclidean distance between the firstregion and the second region, the cosine similarity between the firstregion and the second region, or the like.

The symmetry indicators may be calculated for all candidate sectionimages and thus a group of symmetry indicators may be obtained.Thereafter, a characteristic symmetry indicator which satisfiescharacteristic conditions may be selected from the group of symmetryindicators. In one embodiment, the candidate section image correspondingto the characteristic symmetry indicator is the desired median sagittalsection image of the fetal head.

The “characteristic conditions” herein may be conditions which indicatethe optimal symmetry of the candidate section image. The characteristicconditions may be determined based on the ways for calculating thesymmetry indicators. For example, for the symmetry indicator calculatedusing the formula (12), the smaller the E (i.e. the symmetry indicator),the more similar the image pixels outside both sides of the candidatesection image, i.e., the better the symmetry. Therefore, in this case,the characteristic condition may be that “the symmetry indicator is thesmallest”. While for the symmetry indicator calculated using the formula(13), the larger the E (i.e., the similarity indicator) (for the formula(13), the closer to 1 the E), the more similar the image pixels outsideboth sides of the candidate section image, i.e., the better thesymmetry. Therefore, in this case, the characteristic condition may bethat “the symmetry indicator is the closest to 1” or “the symmetryindicator is the largest”.

When the symmetry indicators are calculated in other ways, thecharacteristic conditions may be accordingly defined. For example, whenthe symmetry indicator is the Euclidean distance between the firstregion and the second region, the characteristic condition may be that“the symmetry indicator is the smallest”. That is, in this case, thesmaller the symmetry indicator (i.e., the smaller the Euclideandistance), the better the symmetry of the first region with the secondregion. When the symmetry indicator is the cosine similarity between thefirst region and the second region, the characteristic condition may bethat “the symmetry indicator is the largest”. That is, the larger thesymmetry indicator (i.e., the larger the cosine similarity), the betterthe symmetry of the first region with the second region.

After the median sagittal section image of the fetal head is extractedusing the methods of the embodiments above, the orientation of the fetalhead and/or the fetal face may be determined with reference to theembodiments above. Based on the determined orientation, the mediansagittal section image in which the fetal head is upside down may beadjusted such that the head is upright and/or the orientation of theface may be marked in the median sagittal section image. Thereafter theadjusted and/or marked median sagittal section image may be displayed.

As described above, some special structures will be shown in the mediansagittal section image of a fetal head. In other words, the mediansagittal section image of a fetal head will contain some specialstructural features. In one embodiment, this characteristic of themedian sagittal section image of a fetal head may be used. A templateimage (or standard reference image) of the median sagittal section imageof a fetal head may be generated using the median sagittal sectionimages of fetal heads which have been obtained previously. Thereafter,in the three-dimensional imaging process, the section images of theobtained three-dimensional volume data may be matched with the templateimage and the similarity between the section images of thethree-dimensional volume data and the template image may be calculated.The section image in the three-dimensional volume data with largestsimilarity with the template image may be the median sagittal sectionimage of the head of the fetus.

For example, in one embodiment, a schematic flow chart of extracting themedian sagittal section image from the three-dimensional volume data maybe as shown in FIG. 24 , which may include obtaining a template image(step 130), selecting candidate section images (step 131), calculating asimilarity indicator of each candidate section image with the templateimage (step 132) and determining a section image corresponding to thesimilarity indicator which satisfy a certain condition (step 133).

In step 130, a template image of the median sagittal section image of afetal head may be obtained. In one embodiment, the template image may begenerated based on the median sagittal section images of the heads ofother fetuses which have been obtained previously, and may be stored ina memory. In the three-dimensional imaging process, the template imagemay be read from the memory. Alternatively, the template image may alsobe generated in the three-dimensional imaging process.

The template image may be one or more. For example, a plurality oftemplate images may be used to match the section images ofthree-dimensional volume data with different sizes.

In the case that a plurality of template images are used, each candidatesection image may be matched with each template image.

After the template images are obtained, a group of candidate sectionimages may be selected from the three-dimensional volume data in step131. The candidate section images may be selected as required. Forexample, in one embodiment, all section images in a certain range in thethree-dimensional volume data which are a certain spacing (or step)apart from each other in one or more certain directions may be selectedas the candidate section images. Herein, the “certain range” may be aangle range with respect to one or more lines and/or planes in thethree-dimensional volume data, or be a distance range with respect toone or more points, lines and/or planes in the three-dimensional volumedata; the “in one or more certain directions” may mean that the normalline of the section image is in said one or more certain directions; the“spacing” or “step” may be a distance spacing or step, or a anglespacing or step.

In one embodiment, all section images which are certain spacing or stepapart from each other in one or more certain directions in the whole ofthe three-dimensional volume data may be selected. In one embodiment,some prior knowledge may be used to assist the selection of thecandidate section images to exclude the section images for which it isimpossible to be the median sagittal section image. For example, sincethe median sagittal section of a fetal head is a longitudinal section(i.e., a section in the direction from the top to the neck of the fetus)which is located at the center position of the fetal head, thelongitudinal section images which are located substantially at thecenter position of the head may be selected as the candidate sectionimages roughly based on the direction of the fetus image in thethree-dimensional volume data. For example, a group of longitudinalsection images which are located at the center position of the head ofthe fetus (for example, all longitudinal section images which are acertain spacing or step apart from each other within a certain region atthe center of the head) may be selected as the candidate section images.

Alternatively, in one embodiment, user inputs which indicate thepossible range of the median sagittal section image may be received.Thereafter, the section images in the range indicated by the user may beselected as the candidate section images.

In one embodiment, all section images which are a certain step apartfrom each other in the whole of the three-dimensional volume data may beselected as the candidate section images. That is, all section images inthe whole three-dimensional volume data may be matched with the templateimage with a certain step.

For example, in one embodiment, when the plane equation in the formula(6) is used, the candidate section images may be selected by determiningthe ranges of the plane parameters θ, φ and ρ and the values of thesteps θstep, φstep and ρstep.

Similarly, when the plane equation aX+bY+cZ+d=0, Z=aX+bY+c or Y=aX+bZ+cis used, the candidate section images may be selected by determining theranges of a, b, c and d and their steps.

For example, when all section images which are a certain step apart fromeach other in the whole of the three-dimensional volume data areselected as the candidate section images, the rang of ρ may be as shownin the formula (7) and the largest ranges of θ and φ may be, forexample, 0°≤θ<360° and −90°≤φ≤90° (with reference to FIG. 16 ). It willbe understood that when the coordinate system is defined in differentway the ranges of the parameters will be different.

The steps θstep, φstep and ρstep may be determined based on theacquisition accuracy required and are not limited by the presentdisclosure. For example, in one embodiment, θ_(step)=1, φ_(step)=1,ρ_(step)=2. It will be understood that the steps may also be othervalues based on the acquisition accuracy required.

As described above, in one embodiment, only one template image is used.In this case, the template image may be generated in a certain size.Before the candidate section images are selected from thethree-dimensional volume data, the methods may further include a step ofadjusting the three-dimensional volume data or the template image. Inthis step, the three-dimensional volume data and the template image maybe adjusted to a same scale space. In other words, the sizes of thecorresponding structures in the three-dimensional volume data and in thetemplate image are made to be approximately the same. By thisadjustment, the corresponding structures in the three-dimensional volumedata and in the template image have approximately same sizes. Thereby,the match is easier to be realized, the match effect is better and thecalculation of the match is reduced.

When adjusting the three-dimensional volume data or the template image,special structural features (for example, the skull ring, etc.) in asection image (for example, the most middle frame of image, i.e., theF/2-th frame of image, or the frame of image near the most middle frameor other frame of image, or other section image) in thethree-dimensional volume data may be detected, and then thethree-dimensional volume data may be converted into the same scale withthe template image by rotation, translation and/or zooming based on thesize of the detected structural features.

Herein, converting the three-dimensional volume data into the same scalewith the template image may refer to making the same or correspondingstructural features in the three-dimensional volume data and in thetemplate image to have same size by conversion.

Herein, the “same” may refer to that they are substantially the same orsimilar, but not be strictly limited to be exactly the same. Rather,there may be a certain difference. In other words, the “same” hereinshould not be strictly interpreted.

In the embodiments, any other suitable method may also be used toadjusting the three-dimensional volume data and the template image tothe same scale space.

After the candidate section images are selected, each of the candidatesection images may be matched with the template image in step 132. Forexample, a similarity indicator of each candidate section image with thetemplate image may be calculated.

The similarity indicator may be used to measure the similarity of thecandidate section image with the template image. In the embodiments, thesimilarity indicator may be calculated using a variety of ways.

For example, in some embodiments, the similarity indicator may be thesum of the absolute value of the difference between the gray values ofthe corresponding points in the candidate section image and in thetemplate image. I.e.,

$\begin{matrix}{E = {\sum\limits_{I_{L},{I_{R} \in \Omega}}{❘{I_{L} - I_{R}}❘}}} & (14)\end{matrix}$

where E is the similarity indicator, Ω is image space of the candidatesection image, I_(L) is the data value of the point in the candidatesection image, and I_(R) is the data value of the point in the templateimage corresponding to the point in the candidate section image. Herein,the “corresponding points in the candidate section image and in thetemplate image” mentioned above may refer to the points in the candidatesection image and in the template image which have same location.

In one embodiment, the similarity indicator may also be the correlationcoefficient between the candidate section image and the template image,i.e.,

$\begin{matrix}{E = \frac{\sum\limits_{I_{L},{I_{R} \in \Omega}}{I_{L}I_{R}}}{\sqrt{\sum\limits_{I_{L} \in \Omega}I_{L}^{2}}\sqrt{\sum\limits_{I_{R} \in \Omega}I_{R}^{2}}}} & (15)\end{matrix}$

where E is the similarity indicator, Ω is the image space of thecandidate section image, I_(L) is the data value of the point in thecandidate section image, and I_(R) is the data value of the point in thetemplate image corresponding to the point in the candidate sectionimage.

The similarity indicator may be defined as described above, but notlimited to. Other definition may also be used.

The similarity indicators of all candidate section images may becalculated and thus a group of similarity indicators may be obtained.Thereafter, a characteristic similarity indicator which satisfiescharacteristic conditions may be selected from the group of similarityindicators. In the embodiments, the candidate section imagecorresponding to the characteristic similarity indicator may be thedesired median sagittal section image of the head of the fetus.

The “characteristic conditions” herein may be conditions which indicatethe best similarity of the candidate section image with the templateimage. The characteristic conditions may be determined based on the waysfor calculating the similarity indicators.

For example, for the similarity indicator calculated using the formula(14), the smaller the E (i.e. the similarity indicator), the moresimilar the candidate section image with the template image, i.e., thebetter the similarity. Therefore, in this case, the characteristiccondition may be that “the similarity indicator is the smallest”.

While for the similarity indicator calculated using the formula (15),the larger the E (i.e., the similarity indicator) (for the formula (15),the closer to 1 the E), the more similar the candidate section imagewith the template image, i.e., the better the similarity. Therefore, inthis case, the characteristic condition may be that “the similarityindicator is the closest to 1” or “the similarity indicator is thelargest”.

When the similarity indicators are calculated in other ways, thecharacteristic conditions may be defined accordingly. For example, whenthe similarity indicator is the Euclidean distance between the candidatesection image and the template image, the characteristic condition maybe that “the similarity indicator is the smallest”. That is, in thiscase, the smaller the similarity indicator (i.e., the smaller theEuclidean distance), the better the similarity of the candidate sectionimage with the template image. When the similarity indicator is thecosine similarity between the candidate section image and the templateimage, the characteristic condition may be that “the similarityindicator is the largest”. That is, the larger the similarity indicator(i.e., the larger the cosine similarity), the better the similarity ofthe candidate section image with the template image.

In some embodiments, after the median sagittal section image of thefetal head is extracted and the orientation of the fetal head (e.g., theorientation of the top of the head and/or the face) is determined, themedian sagittal section image may be adjusted to desired orientationand/or the orientation of the fetal head may be marked on the mediansagittal section image. Thereafter, the adjusted or marked mediansagittal section image may be displayed on the display.

In some embodiments, the methods for extracting the median sagittalsection image of the fetal head will not be limited to those describedabove. Any other suitable methods for extracting the median sagittalsection image of the fetal head may also be used.

In some embodiments, the terms such as “top”, “down”, “front”, “rear”,“left” and “right”, etc. have been used, where “top” and “down” may bedefined respectively as the orientations where the head in the image isupright and upside down according to the human observation habits,“front” and “rear” may be defined respectively as the orientationscorresponding to the fetal face and the fetal back according to thehuman observation habits, and “left” and “right” may be relative. “Left”may correspond to “front”. Alternatively, “left” may correspond to“rear”. However, the present disclosure will not be limited thereto.

The person skilled in the art will understand that all or a part of thesteps of the methods in the embodiments above may be implemented byinstructing related hardware, such as a processor, to execute programsstored in a non-transitory computer readable storage medium. Thecomputer readable storage medium may include read-only memory, randomaccess memory, disk or disc, etc.

The present disclosure has been described in detail with reference tospecific embodiments. However, the implementation of the presentdisclosure will not be limited thereto. Many substitutions andmodifications may be made by the person ordinarily skilled in the art towhich the present disclosure belongs without departing from the conceptsof the present disclosure.

We claim:
 1. A three-dimensional ultrasound imaging method, comprising:transmitting ultrasound waves to a fetal head; receiving ultrasoundechoes to obtain ultrasound echo signals; obtaining a three-dimensionalvolume data of the fetal head according to the ultrasound echo signals;extracting a median sagittal section image from the three-dimensionalvolume data based on characteristics of a median sagittal section of afetal head; detecting image regions representing specific tissue areasof the fetal head in at least one of the extracted median sagittalsection image and a section image which is parallel to or intersectswith the extracted median sagittal section image; determining anorientation of the fetal head in the at least one of the extractedmedian sagittal section image and the section image which is parallel toor intersects with the extracted median sagittal section image based onthe image regions; and rotating the extracted median sagittal sectionimage based on the orientation of the fetal head such that in therotated and extracted median sagittal section image the fetal head is ina pre-set orientation, or marking the orientation of the fetal head inthe extracted median sagittal section image.
 2. The method of claim 1,wherein detecting the image regions representing specific tissue areasof the fetal head in at least one of the extracted median sagittalsection image and the section image which is parallel to or intersectswith the extracted median sagittal section image and determining theorientation of the fetal head based on the image regions comprises:detecting at least two image regions representing at least two specifictissue areas of the fetal head which have certain mutual positionalrelationship in at least one of the extracted median sagittal sectionimage and the section image which is parallel to or intersects with theextracted median sagittal section image, and determining the orientationof the fetal head based on the mutual positional relationship betweenthe at least two image regions.
 3. The method of claim 1, whereindetecting the image regions representing specific tissue areas of thefetal head in at least one of the extracted median sagittal sectionimage and the section image which is parallel to or intersects with theextracted median sagittal section image and determining the orientationof the fetal head based on the image regions comprises: detecting animage region representing a specific tissue region in the fetal headwhich has directional characteristics in at least one of the extractedmedian sagittal section image and the section image which is parallel toor intersects with the extracted median sagittal section image, anddetermining the orientation of the fetal head based on at least one of aposition and a shape of the image region.
 4. The method of claim 3,wherein the image region is a region representing a skull of the fetalhead, and wherein detecting an image region representing the specifictissue region in the fetal head which has directional characteristics inat least one of the extracted median sagittal section image and thesection image which is parallel to or intersects with the extractedmedian sagittal section image and determining the orientation of thefetal head based on the image region comprises: selecting at least oneof the extracted median sagittal section image and at least one sectionimage parallel to the extracted median sagittal section image andextracting characteristics regions corresponding to the skull from theselected section image based on characteristics of the skull inultrasound image to obtain candidate regions representing the skull;determining an orientation of the skull based on the candidate regions;and determining the orientation of the fetal head based on theorientation of the skull.
 5. The method of claim 4, wherein: extractingthe characteristics regions corresponding to the skull comprisesextracting regions whose grayscale values are greater than a pre-setthreshold in the selected section image as the characteristics regions,or designing operators based on differential, calculating a convolutionof the operators with the selected section image and selecting regionswhose convolution values are greater than a pre-set threshold as thecharacteristics regions; obtaining the candidate regions representingthe skull comprises selecting at least one characteristics region ofwhich a sum of characteristics values are maximum as the candidateregions, or selecting at least one characteristics region of whichaverage characteristics values are maximum as the candidate regions, orextracting characteristics from the characteristics regions, inputtingthe extracted characteristics into a pre-trained classifier anddetermining the candidate regions based on classification results; anddetermining the orientation of the skull comprises determining theorientation of the skull based on bending direction of the skull.
 6. Themethod of claim 3, wherein the image region is a region representing acavum septi pellucid of the fetal head, and wherein detecting an imageregion representing the specific tissue region in the fetal head whichhas directional characteristics in at least one of the extracted mediansagittal section image and the section image which is parallel to orintersects with the extracted median sagittal section image anddetermining the orientation of the fetal head based on the image regioncomprises: detecting a connected region corresponding to the cavum septipellucid from the extracted median sagittal section image based oncharacteristics of the cavum septi pellucid in ultrasound image;determining an orientation of the cavum septi pellucid based on theconnected region; and determining the orientation of the fetal headbased on the orientation of the cavum septi pellucid.
 7. The method ofclaim 6, wherein detecting the connected region corresponding to thecavum septi pellucid from the extracted median sagittal section imagecomprises segmenting the extracted median sagittal section image toobtain at least one region and selecting a region which is most similarto the cavum septi pellucid from the at least one region based oncharacteristics of the at least one region as the connected regioncorresponding to the cavum septi pellucid.
 8. The method of claim 3,wherein the image region is a region representing a cavum septi pellucidof the fetal head, and wherein detecting an image region representingthe specific tissue region in the fetal head which has directionalcharacteristics in at least one of the extracted median sagittal sectionimage and the section image which is parallel to or intersects with theextracted median sagittal section image and determining the orientationof the fetal head based on the image region comprises: detecting a cavumsepti pellucid region from the extracted median sagittal section imagebased on characteristics of the cavum septi pellucid in ultrasoundimage; selecting at least one frame of horizontal section image form thethree-dimensional volume data, performing an ellipsoid or ellipsedetection on the horizontal section image and obtaining a center of thedetected ellipse or ellipsoid; and comparing a coordinate of the cavumsepti pellucid region and a coordinate of the center and determining theorientation of the fetal head based on the comparison.
 9. The method ofclaim 8, wherein detecting the cavum septi pellucid region from theextracted median sagittal section image comprises: segmenting theextracted median sagittal section image to obtain at least one regionand selecting a region which is most similar to the cavum septi pellucidfrom the at least one region based on characteristics of the at leastone region as the cavum septi pellucid region; and performing theellipsoid or ellipse detection on the horizontal section image comprisesdesigning operators based on differential, extracting characteristics ofthe horizontal section image using the operators and performing theellipsoid or ellipse detection on the extracted characteristics.
 10. Athree-dimensional ultrasound imaging device, comprising: a probe whichtransmits ultrasound waves to a fetal head and receives ultrasoundechoes to obtain ultrasound echo signals; a three-dimensional imagingunit which obtains a three-dimensional volume data of the fetal headusing the ultrasound echo signals, extracts a median sagittal sectionimage from the three-dimensional volume data based on characteristics ofa median sagittal section of a fetal head, determines an orientation ofthe fetal head in the at least one of the extracted median sagittalsection image and the section image which is parallel to or intersectswith the extracted median sagittal section image based on the imageregions, and rotates the extracted median sagittal section image basedon the orientation of the fetal head such that in the rotated andextracted median sagittal section image the fetal head is in a pre-setorientation or marks the orientation of the fetal head in the extractedmedian sagittal section image; and a display which displays theextracted median sagittal section image.
 11. The device of claim 10,wherein the three-dimensional imaging unit further detects at least twoimage regions representing at least two specific tissue areas of thefetal head which have certain mutual positional relationship in at leastone of the extracted median sagittal section image and the section imagewhich is parallel to or intersects with the extracted median sagittalsection image, and determining the orientation of the fetal head basedon the mutual positional relationship between the at least two imageregions.
 12. The device of claim 10, wherein the three-dimensionalimaging unit further detects an image region representing a specifictissue region in the fetal head which has directional characteristics inat least one of the extracted median sagittal section image and asection image which is parallel to or intersects with the extractedmedian sagittal section image, and determining the orientation of thefetal head based on at least one of a position and a shape of the imageregion.
 13. A three-dimensional ultrasound imaging method, comprising:transmitting ultrasound waves to a fetal head; receiving ultrasoundechoes to obtain ultrasound echo signals; obtaining a three-dimensionalvolume data of the fetal head according to the ultrasound echo signals;extracting a median sagittal section image from the three-dimensionalvolume data based on characteristics of a median sagittal section of afetal head; detecting image regions representing specific tissue areasof the fetal head in at least one of the extracted median sagittalsection image and a section image which is parallel to or intersectswith the extracted median sagittal section image; and determining anorientation of the fetal head in the at least one of the extractedmedian sagittal section image and the section image which is parallel toor intersects with the extracted median sagittal section image based onthe image regions.